tabla_prom_reg <- data.frame()

for (i in reg_20) {
  casen_fh <- filter(casen2020, casen2020$region == i)
  
casen_fh <- casen_fh[,c("region","folio","ytrabajocorh")]
casen_fh$folio <- as.character(casen_fh$folio)
  
tabla <- table(casen_fh$folio)
tabla <- as.data.frame(tabla)
colnames(tabla) <- c("folio","freq_folio")
repetidos2 <- merge(x=casen_fh, y=tabla, by = "folio", all.x = T)
repetidos2 <- repetidos2[,c(2,1,4,3)]
rr <- repetidos2[!duplicated(repetidos2$folio ), ]

tabla_reg <- data.frame(
  region = i,
  promedio = mean(rr$ytrabajocorh)
)

tabla_prom_reg <- rbind(tabla_prom_reg, tabla_reg)

}

tabla_ing_aut <- read_xlsx("ingreso_del_trabajo_oficial_2020.xlsx")
names(tabla_ing_aut)[3] <- "region" 
names(tabla_ing_aut)[1] <- "region_c" 
 
tabla_unida = merge(x = tabla_prom_reg, y = tabla_ing_aut, by = "region", all.x = T)
tabla_unida <- tabla_unida[,-c(3)]
kbl(tabla_unida) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

p <- plot_ly(tabla_unida, width = 1200, x = ~
region, y = ~promedio) %>%
  add_lines() 
p <- p %>% add_trace(y = ~`Ingreso del trabajo`, name = 'ingreso del trabajo oficial', mode = 'markers')
p 

tabla_prom_reg <- data.frame()

for (i in reg_20) {
  casen_fh <- filter(casen2020, casen2020$region == i)
  
casen_fh <- casen_fh[,c("region","folio","yautcorh")]
casen_fh$folio <- as.character(casen_fh$folio)
  
tabla <- table(casen_fh$folio)
tabla <- as.data.frame(tabla)
colnames(tabla) <- c("folio","freq_folio")
repetidos2 <- merge(x=casen_fh, y=tabla, by = "folio", all.x = T)
repetidos2 <- repetidos2[,c(2,1,4,3)]
rr <- repetidos2[!duplicated(repetidos2$folio ), ]

tabla_reg <- data.frame(
  region = i,
  promedio = mean(rr$yautcorh)
)

tabla_prom_reg <- rbind(tabla_prom_reg, tabla_reg)

}

tabla_ing_aut <- read_xlsx("ingreso_autonomo_2020_3.xlsx")

names(tabla_ing_aut)[3] <- "region"
 
tabla_unida = merge(x = tabla_prom_reg, y = tabla_ing_aut, by = "region", all.x = T)
tabla_unida <- tabla_unida[,-c(3)]
 
kbl(tabla_unida) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

p <- plot_ly(tabla_unida, width = 1200, x = ~
region, y = ~promedio) %>%
  add_lines() 
p <- p %>% add_trace(y = ~ingreso_autonomo, name = 'ingreso autonomo oficial', mode = 'markers')
p 

tabla_prom_reg <- data.frame()

for (i in reg_20) {
  casen_fh <- filter(casen2020, casen2020$region == i)
  
casen_fh <- casen_fh[,c("region","folio","ysubh")]
casen_fh$folio <- as.character(casen_fh$folio)
  
tabla <- table(casen_fh$folio)
tabla <- as.data.frame(tabla)
colnames(tabla) <- c("folio","freq_folio")
repetidos2 <- merge(x=casen_fh, y=tabla, by = "folio", all.x = T)
repetidos2 <- repetidos2[,c(2,1,4,3)]
rr <- repetidos2[!duplicated(repetidos2$folio ), ]

tabla_reg <- data.frame(
  region = i,
  promedio = mean(rr$ysubh)
)

tabla_prom_reg <- rbind(tabla_prom_reg, tabla_reg)

}

tabla_ing_aut <- read_xlsx("subsidios_monetarios_oficial_2020.xlsx") 
names(tabla_ing_aut)[3] <- "region" 
names(tabla_ing_aut)[1] <- "region_c"
 
tabla_unida = merge(x = tabla_prom_reg, y = tabla_ing_aut, by = "region", all.x = T)
tabla_unida <- tabla_unida[,-c(3)]
kbl(tabla_unida) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

p <- plot_ly(tabla_unida, width = 1200, x = ~
region, y = ~promedio) %>%
  add_lines() 
p <- p %>% add_trace(y = ~`Subsidios monetarios`, name = 'ingreso del subsidio monetarios oficial', mode = 'markers')
p 

tabla_prom_reg <- data.frame()

for (i in reg_20) {
  casen_fh <- filter(casen2020, casen2020$region == i)
  
casen_fh <- casen_fh[,c("region","folio","ymonecorh")]
casen_fh$folio <- as.character(casen_fh$folio)
  
tabla <- table(casen_fh$folio)
tabla <- as.data.frame(tabla)
colnames(tabla) <- c("folio","freq_folio")
repetidos2 <- merge(x=casen_fh, y=tabla, by = "folio", all.x = T)
repetidos2 <- repetidos2[,c(2,1,4,3)]
rr <- repetidos2[!duplicated(repetidos2$folio ), ]

tabla_reg <- data.frame(
  region = i,
  promedio = mean(rr$ymonecorh)
)

tabla_prom_reg <- rbind(tabla_prom_reg, tabla_reg)

}

tabla_ing_aut <- read_xlsx("ingreso_monetario_oficial_2020.xlsx") 
names(tabla_ing_aut)[3] <- "region" 
names(tabla_ing_aut)[1] <- "region_c"
 
tabla_unida = merge(x = tabla_prom_reg, y = tabla_ing_aut, by = "region", all.x = T)
tabla_unida <- tabla_unida[,-c(3)]
kbl(tabla_unida) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

p <- plot_ly(tabla_unida, width = 1200, x = ~
region, y = ~promedio) %>%
  add_lines() 
p <- p %>% add_trace(y = ~`Ingreso monetario`, name = 'ingreso monetario oficial', mode = 'markers')
p 

tabla_prom_reg <- data.frame()

for (i in reg_17) {
  casen_fh <- filter(casen2017, casen2017$region == i)
  
casen_fh <- casen_fh[,c("region","folio","ytrabajocorh")]
casen_fh$folio <- as.character(casen_fh$folio)
  
tabla <- table(casen_fh$folio)
tabla <- as.data.frame(tabla)
colnames(tabla) <- c("folio","freq_folio")
repetidos2 <- merge(x=casen_fh, y=tabla, by = "folio", all.x = T)
repetidos2 <- repetidos2[,c(2,1,4,3)]
rr <- repetidos2[!duplicated(repetidos2$folio ), ]

tabla_reg <- data.frame(
  region = i,
  promedio_2017 = mean(rr$ytrabajocorh)
)

tabla_prom_reg <- rbind(tabla_prom_reg, tabla_reg)

}

tabla_ing_aut <- read_xlsx("ingreso_del_trabajo_oficial_2017.xlsx")
names(tabla_ing_aut)[3] <- "region" 
names(tabla_ing_aut)[1] <- "region_c" 
 
tabla_2017 = merge(x = tabla_prom_reg, y = tabla_ing_aut, by = "region", all.x = T)
tabla_2017 <- tabla_2017[,-c(3)]
kbl(tabla_2017) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

p <- plot_ly(tabla_2017, width = 1200, x = ~
region, y = ~promedio_2017) %>%
  add_lines() 
p <- p %>% add_trace(y = ~`Ingreso del trabajo 2017`, name = 'ingreso del trabajo 2017 oficial', mode = 'markers')
p 

# tabla_ing_aut1 <- read_xlsx("ingreso_del_trabajo_oficial_2020.xlsx")
# names(tabla_ing_aut1)[3] <- "region" 
# names(tabla_ing_aut1)[1] <- "region_c" 
#  
# tabla_ing_aut2 <- read_xlsx("ingreso_del_trabajo_oficial_2017.xlsx")
# names(tabla_ing_aut2)[3] <- "region" 
# names(tabla_ing_aut2)[1] <- "region_c" 
#  
#  
# tabla_2 = merge(x = tabla_ing_aut2, y = tabla_ing_aut1, by = "region_c", all.x = T)
# tabla_2 <- tabla_2[,-c(3)]
#  
# tabla_ing_aut3 <- read_xlsx("ingresos_del_trabajo_global.xlsx")
# tabla_ing_aut3 <- tabla_ing_aut3[,-c(2)]
# names(tabla_ing_aut3)[7] <- "region"
# names(tabla_ing_aut3)[1] <- "region_c"
#  
# tabla_3 = merge(x = tabla_ing_aut3, y = tabla_2, by = "region_c", all.x = T)
# tabla_3 <- tabla_3[,-c(7)]
# names(tabla_3)[7] <- "2017"
# names(tabla_3)[8] <- "2020"
# names(tabla_3)[9] <- "region"
# 
# kbl(tabla_3) %>%
#   kable_styling(bootstrap_options = c("striped", "hover")) %>%
#   kable_paper() %>%
#   scroll_box(width = "100%", height = "300px")

datos_oficiales <- read_xlsx("ingresos_oficiales_2006-2020.xlsx")
datos_oficiales <- cbind(datos_oficiales[,c(1,9,10)],mutate_if(datos_oficiales[,2:8], is.character, as.numeric))
datos_oficiales <- filter(datos_oficiales, grepl("trabajo", ingreso) )


kbl(datos_oficiales) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

tabla_3 <- datos_oficiales

#### 2020

tabla_prom_reg <- data.frame()

for (i in reg_20) {
  casen_fh <- filter(casen2020, casen2020$region == i)
  
casen_fh <- casen_fh[,c("region","folio","ytrabajocorh")]
casen_fh$folio <- as.character(casen_fh$folio)
  
tabla <- table(casen_fh$folio)
tabla <- as.data.frame(tabla)
colnames(tabla) <- c("folio","freq_folio")
repetidos2 <- merge(x=casen_fh, y=tabla, by = "folio", all.x = T)
repetidos2 <- repetidos2[,c(2,1,4,3)]
rr <- repetidos2[!duplicated(repetidos2$folio ), ]

tabla_reg <- data.frame(
  region = i,
  calculado_2020 = mean(rr$ytrabajocorh)
)

tabla_prom_reg <- rbind(tabla_prom_reg, tabla_reg)

}

# tabla_ing_aut <- read_xlsx("ingreso_del_trabajo_oficial_2020.xlsx")
# names(tabla_ing_aut)[3] <- "region" 
# names(tabla_ing_aut)[1] <- "region_c" 
 tabla_ing_aut <- tabla_3[,c("region","publicado_2020")]
tabla_2020 = merge(x = tabla_prom_reg, y = tabla_ing_aut, by = "region", all.x = T)
tabla_2020 <- tabla_2020[,-c(3)]
 
tabla_prom_reg <- data.frame()

for (i in reg_17) {
  casen_fh <- filter(casen2017, casen2017$region == i)
  
casen_fh <- casen_fh[,c("region","folio","ytrabajocorh")]
casen_fh$folio <- as.character(casen_fh$folio)
  
tabla <- table(casen_fh$folio)
tabla <- as.data.frame(tabla)
colnames(tabla) <- c("folio","freq_folio")
repetidos2 <- merge(x=casen_fh, y=tabla, by = "folio", all.x = T)
repetidos2 <- repetidos2[,c(2,1,4,3)]
rr <- repetidos2[!duplicated(repetidos2$folio ), ]

tabla_reg <- data.frame(
  region = i,
  calculado_2017 = mean(rr$ytrabajocorh)
)

tabla_prom_reg <- rbind(tabla_prom_reg, tabla_reg)

}

# tabla_ing_aut <- read_xlsx("ingreso_del_trabajo_oficial_2017.xlsx")
# names(tabla_ing_aut)[3] <- "region" 
# names(tabla_ing_aut)[1] <- "region_c" 
  tabla_ing_aut <- tabla_3[,c("region","publicado_2017")]
  
tabla_2017 = merge(x = tabla_prom_reg, y = tabla_ing_aut, by = "region", all.x = T)
tabla_2017 <- tabla_2017[,-c(3)]
  
#### 2015

tabla_prom_reg <- data.frame()

for (i in reg_15) {
  casen_fh <- filter(casen2015, casen2015$region == i)
  
casen_fh <- casen_fh[,c("region","folio.x","ytrabajoCorh")]
casen_fh$folio.x <- as.character(casen_fh$folio.x)
  
tabla <- table(casen_fh$folio.x)
tabla <- as.data.frame(tabla)
colnames(tabla) <- c("folio.x","freq_folio")
repetidos2 <- merge(x=casen_fh, y=tabla, by = "folio.x", all.x = T)
repetidos2 <- repetidos2[,c(2,1,4,3)]
rr <- repetidos2[!duplicated(repetidos2$folio.x ), ]

tabla_reg <- data.frame(
  region = i,
  promedio_2015 = mean(rr$ytrabajoCorh)
)

tabla_prom_reg <- rbind(tabla_prom_reg, tabla_reg)

}
tabla_prom_reg <- rbind(tabla_prom_reg,c("Región de Ñuble",NA))
# tabla_ing_aut <- read_xlsx("ingreso_del_trabajo_oficial_2017.xlsx")
# names(tabla_ing_aut)[3] <- "region" 
# names(tabla_ing_aut)[1] <- "region_c" 
  tabla_ing_aut <- tabla_3[,c("region","publicado_2015")]
  
tabla_2015 = merge(x = tabla_prom_reg, y = tabla_ing_aut, by = "region", all.x = T)
tabla_2015 <- tabla_2015[,-c(3)]
tabla_2015$promedio_2015 <- as.numeric(tabla_2015$promedio_2015)
tabla_2015$region[tabla_2015$region == "Región del Libertador Gral. Bernardo O Higgins"] <- "Región del Libertador Gral. Bernardo O'Higgins"
 
#### 2013

tabla_prom_reg <- data.frame()

for (i in reg_13) {
  casen_fh <- filter(casen2013, casen2013$region == i)
  
casen_fh <- casen_fh[,c("region","folio","ytrabajoCorh")]
casen_fh$folio <- as.character(casen_fh$folio)
  
tabla <- table(casen_fh$folio)
tabla <- as.data.frame(tabla)
colnames(tabla) <- c("folio","freq_folio")
repetidos2 <- merge(x=casen_fh, y=tabla, by = "folio", all.x = T)
repetidos2 <- repetidos2[,c(2,1,4,3)]
rr <- repetidos2[!duplicated(repetidos2$folio ), ]

tabla_reg <- data.frame(
  region = i,
  calculado_2013 = mean(rr$ytrabajoCorh)
)

tabla_prom_reg <- rbind(tabla_prom_reg, tabla_reg)

}
tabla_prom_reg <- rbind(tabla_prom_reg,c("Región de Ñuble",NA))
# tabla_ing_aut <- read_xlsx("ingreso_del_trabajo_oficial_2017.xlsx")
# names(tabla_ing_aut)[3] <- "region" 
# names(tabla_ing_aut)[1] <- "region_c" 
  tabla_ing_aut <- tabla_3[,c("region","publicado_2013")]
  
tabla_2013 = merge(x = tabla_prom_reg, y = tabla_ing_aut, by = "region", all.x = T)
tabla_2013 <- tabla_2013[,-c(3)]

tabla_2013$region[tabla_2013$region == "I. Tarapaca"] <- "Región de Tarapacá"
tabla_2013$region[tabla_2013$region == "II. Antofagasta"] <- "Región de Antofagasta"
tabla_2013$region[tabla_2013$region == "III. Atacama"] <- "Región de Atacama"
tabla_2013$region[tabla_2013$region == "IV. Coquimbo"] <- "Región de Coquimbo"
tabla_2013$region[tabla_2013$region == "IX. La Araucanía"] <- "Región de La Araucanía"
tabla_2013$region[tabla_2013$region == "Metropolitana"] <- "Región Metropolitana de Santiago"
tabla_2013$region[tabla_2013$region == "V. Valparaíso"] <- "Región de Valparaíso"
tabla_2013$region[tabla_2013$region == "VI. O Higgins"] <- "Región del Libertador Gral. Bernardo O'Higgins"
tabla_2013$region[tabla_2013$region == "VII. Maule"] <- "Región del Maule"
tabla_2013$region[tabla_2013$region == "VIII. Biobío"] <- "Región del Biobío"
tabla_2013$region[tabla_2013$region == "X. Los Lagos"] <- "Región de Los Lagos"
tabla_2013$region[tabla_2013$region == "XI. Aysén"] <- "Región de Aysén del Gral. Carlos Ibáñez del Campo"
tabla_2013$region[tabla_2013$region == "XII. Magallanes"] <- "Región de Magallanes y de la Antártica Chilena"
tabla_2013$region[tabla_2013$region == "XIV. Los Ríos"] <- "Región de Los Ríos"
tabla_2013$region[tabla_2013$region == "XV. Arica y Parinacota"] <- "Región de Arica y Parinacota"
tabla_2013$calculado_2013 <- as.numeric(tabla_2013$calculado_2013)

#### 2011

tabla_prom_reg <- data.frame()

for (i in reg_11) {
  casen_fh <- filter(casen2011, casen2011$region == i)
  
casen_fh <- casen_fh[,c("region","folio","ytrabhaj")]
casen_fh$folio <- as.character(casen_fh$folio)
  
tabla <- table(casen_fh$folio)
tabla <- as.data.frame(tabla)
colnames(tabla) <- c("folio","freq_folio")
repetidos2 <- merge(x=casen_fh, y=tabla, by = "folio", all.x = T)
repetidos2 <- repetidos2[,c(2,1,4,3)]
rr <- repetidos2[!duplicated(repetidos2$folio ), ]

tabla_reg <- data.frame(
  region = i,
  calculado_2011 = mean(rr$ytrabhaj)
)

tabla_prom_reg <- rbind(tabla_prom_reg, tabla_reg)

}
tabla_prom_reg <- rbind(tabla_prom_reg,c("Región de Ñuble",NA))
# tabla_ing_aut <- read_xlsx("ingreso_del_trabajo_oficial_2017.xlsx")
# names(tabla_ing_aut)[3] <- "region" 
# names(tabla_ing_aut)[1] <- "region_c" 
  tabla_ing_aut <- tabla_3[,c("region","publicado_2011")]
  
tabla_2011 = merge(x = tabla_prom_reg, y = tabla_ing_aut, by = "region", all.x = T)
tabla_2011 <- tabla_2011[,-c(3)]
tabla_2011$region[tabla_2011$region == "Antofagasta"] <- "Región de Antofagasta"
tabla_2011$region[tabla_2011$region == "Araucanía"] <- "Región de La Araucanía"
tabla_2011$region[tabla_2011$region == "Arica y Parinacota"] <- "Región de Arica y Parinacota"
tabla_2011$region[tabla_2011$region == "Aysén"] <- "Región de Aysén del Gral. Carlos Ibáñez del Campo"
tabla_2011$region[tabla_2011$region == "Bio Bio"] <- "Región del Biobío"
tabla_2011$region[tabla_2011$region == "Copiapó"] <- "Región de Atacama"
tabla_2011$region[tabla_2011$region == "Coquimbo"] <- "Región de Coquimbo"
tabla_2011$region[tabla_2011$region == "Los Lagos"] <- "Región de Los Lagos"
tabla_2011$region[tabla_2011$region == "Los Ríos"] <- "Región de Los Ríos"
tabla_2011$region[tabla_2011$region == "Magallanes"] <- "Región de Magallanes y de la Antártica Chilena"
tabla_2011$region[tabla_2011$region == "Maule"] <- "Región del Maule"
tabla_2011$region[tabla_2011$region == "Metropolitana"] <- "Región Metropolitana de Santiago"
tabla_2011$region[tabla_2011$region == "O Higgins"] <- "Región del Libertador Gral. Bernardo O'Higgins"
tabla_2011$region[tabla_2011$region == "Tarapacá"] <- "Región de Tarapacá"
tabla_2011$region[tabla_2011$region == "Valparaíso"] <- "Región de Valparaíso"
tabla_2011$calculado_2011 <- as.numeric(tabla_2011$calculado_2011)
 
#### 2009

tabla_prom_reg <- data.frame()

for (i in reg_09) {
  casen_fh <- filter(casen2009, casen2009$REGION == i)
  
casen_fh <- casen_fh[,c("REGION","FOLIO","YTRABHAJ")]
casen_fh$FOLIO <- as.character(casen_fh$FOLIO)
  
tabla <- table(casen_fh$FOLIO)
tabla <- as.data.frame(tabla)
colnames(tabla) <- c("FOLIO","freq_folio")
repetidos2 <- merge(x=casen_fh, y=tabla, by = "FOLIO", all.x = T)
repetidos2 <- repetidos2[,c(2,1,4,3)]
rr <- repetidos2[!duplicated(repetidos2$FOLIO ), ]

tabla_reg <- data.frame(
  region = i,
 calculado_2009 = mean(rr$YTRABHAJ)
)

tabla_prom_reg <- rbind(tabla_prom_reg, tabla_reg)

}
tabla_prom_reg <- rbind(tabla_prom_reg,c("Región de Ñuble",NA))
# tabla_ing_aut <- read_xlsx("ingreso_del_trabajo_oficial_2017.xlsx")
# names(tabla_ing_aut)[3] <- "region" 
# names(tabla_ing_aut)[1] <- "region_c" 
  tabla_ing_aut <- tabla_3[,c("region","publicado_2009")]
  
tabla_2009 = merge(x = tabla_prom_reg, y = tabla_ing_aut, by = "region", all.x = T)
tabla_2009 <- tabla_2009[,-c(3)]
tabla_2009$region[tabla_2009$region == "Antofagasta"] <- "Región de Antofagasta"
tabla_2009$region[tabla_2009$region == "Arica y Parinacota"] <- "Región de Arica y Parinacota"
tabla_2009$region[tabla_2009$region == "Atacama"] <- "Región de Atacama"
tabla_2009$region[tabla_2009$region == "Aysén"] <- "Región de Aysén del Gral. Carlos Ibáñez del Campo"
tabla_2009$region[tabla_2009$region == "Bío Bío"] <- "Región del Biobío"
tabla_2009$region[tabla_2009$region == "Coquimbo"] <- "Región de Coquimbo"
tabla_2009$region[tabla_2009$region == "La Araucanía"] <- "Región de La Araucanía"
tabla_2009$region[tabla_2009$region == "Libertador Bernardo O´Higgins"] <- "Región del Libertador Gral. Bernardo O'Higgins"
tabla_2009$region[tabla_2009$region == "Los Lagos"] <- "Región de Los Lagos"
tabla_2009$region[tabla_2009$region == "Los Rios"] <- "Región de Los Ríos"
tabla_2009$region[tabla_2009$region == "Magallanes Y La Antártica Chilena"] <- "Región de Magallanes y de la Antártica Chilena"
tabla_2009$region[tabla_2009$region == "Maule"] <- "Región del Maule"
tabla_2009$region[tabla_2009$region == "Región Metropolitana"] <- "Región Metropolitana de Santiago"
tabla_2009$region[tabla_2009$region == "Tarapacá"] <- "Región de Tarapacá"
tabla_2009$region[tabla_2009$region == "Valparaíso"] <- "Región de Valparaíso"
tabla_2009$calculado_2009 <- as.numeric(tabla_2009$calculado_2009)

tabla_2017$region[tabla_2017$region == "Región de Ayséndel Gral. Carlos Ibáñez del Campo"] <- "Región de Aysén del Gral. Carlos Ibáñez del Campo"
tabla_2017$region[tabla_2017$region == "Región del Libertador Gral. Bernardo O’Higgins"] <- "Región del Libertador Gral. Bernardo O'Higgins"
tabla_2020$region[tabla_2020$region == "Región de Ayséndel Gral. Carlos Ibáñez del Campo"] <- "Región de Aysén del Gral. Carlos Ibáñez del Campo"
tabla_2020$region[tabla_2020$region == "Región del Libertador Gral. Bernardo O’Higgins"] <- "Región del Libertador Gral. Bernardo O'Higgins"

ingresos  = merge(x = tabla_2017, y = tabla_2020, by = "region", all.x = T)
ingresos  = merge(x = tabla_2015, y = ingresos, by = "region", all.x = T)
ingresos  = merge(x = tabla_2013, y = ingresos, by = "region", all.x = T)
ingresos  = merge(x = tabla_2011, y = ingresos, by = "region", all.x = T)
ingresos  = merge(x = tabla_2009, y = ingresos, by = "region", all.x = T)

kbl(ingresos) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

tabla_3$region[tabla_3$region == "Región de Ayséndel Gral. Carlos Ibáñez del Campo"] <- "Región de Aysén del Gral. Carlos Ibáñez del Campo"
tabla_3$region[tabla_3$region == "Región del Libertador Gral. Bernardo O’Higgins"] <- "Región del Libertador Gral. Bernardo O'Higgins"

tabla_f = merge( x = ingresos, y = tabla_3, by = "region" , all.x = T)


tabla_f%>% datatable(extensions = 'Buttons',class = 'cell-border stripe',
          options = list(
            dom = 'Blfrtip',
            buttons = c('copy', 'csv', 'excel', 'pdf', 'print'),
            pageLength = 5,
            autoWidth = TRUE,
            
            scrollX = TRUE,
            lengthMenu = list(c(10,25,50,-1),c(10,25,50,"All"))
)) 

tabla_f

tabla_f$por_01 <- 100*tabla_f$calculado_2009/tabla_f$calculado_2011

names (tabla_f)[5] = "calculado_2015"

tabla_f

 #### 2020

tabla_prom_reg <- data.frame()

for (i in reg_20) {
  casen_fh <- filter(casen2020, casen2020$region == i)
  
casen_fh <- casen_fh[,c("region","folio","yautcorh")]
casen_fh$folio <- as.character(casen_fh$folio)
  
tabla <- table(casen_fh$folio)
tabla <- as.data.frame(tabla)
colnames(tabla) <- c("folio","freq_folio")
repetidos2 <- merge(x=casen_fh, y=tabla, by = "folio", all.x = T)
repetidos2 <- repetidos2[,c(2,1,4,3)]
rr <- repetidos2[!duplicated(repetidos2$folio ), ]

tabla_reg <- data.frame(
  region = i,
  promedio_2020 = mean(rr$yautcorh)
)

tabla_prom_reg <- rbind(tabla_prom_reg, tabla_reg)

}

tabla_ing_aut <- read_xlsx("ingreso_autonomo_2020_3.xlsx")
names(tabla_ing_aut)[3] <- "region" 
names(tabla_ing_aut)[1] <- "region_c" 
 
tabla_2020 = merge(x = tabla_prom_reg, y = tabla_ing_aut, by = "region", all.x = T)
tabla_2020 <- tabla_2020[,-c(3)]

 #### 2017

tabla_prom_reg <- data.frame()

for (i in reg_17) {
  casen_fh <- filter(casen2017, casen2017$region == i)
  
casen_fh <- casen_fh[,c("region","folio","yautcorh")]
casen_fh$folio <- as.character(casen_fh$folio)
  
tabla <- table(casen_fh$folio)
tabla <- as.data.frame(tabla)
colnames(tabla) <- c("folio","freq_folio")
repetidos2 <- merge(x=casen_fh, y=tabla, by = "folio", all.x = T)
repetidos2 <- repetidos2[,c(2,1,4,3)]
rr <- repetidos2[!duplicated(repetidos2$folio ), ]

tabla_reg <- data.frame(
  region = i,
  promedio_2017 = mean(rr$yautcorh)
)

tabla_prom_reg <- rbind(tabla_prom_reg, tabla_reg)

}
 
tabla_ing_aut <- read_xlsx("ingreso_autonomo_oficial_2017.xlsx")
names(tabla_ing_aut)[3] <- "region" 
names(tabla_ing_aut)[1] <- "region_c" 
 
tabla_2017 = merge(x = tabla_prom_reg, y = tabla_ing_aut, by = "region", all.x = T)
tabla_2017 <- tabla_2017[,-c(3)]
tabla_2017$region[tabla_2017$region == "Región de Ayséndel Gral. Carlos Ibáñez del Campo"] <- "Región de Aysén del Gral. Carlos Ibáñez del Campo"
tabla_2017$region[tabla_2017$region == "Región del Libertador Gral. Bernardo O’Higgins"] <- "Región del Libertador Gral. Bernardo O'Higgins"

ingresos_2017_2020 <- cbind(tabla_2017,tabla_2020)
ingresos_2017_2020 <- ingresos_2017_2020[,-c(3,4,6)]
write_xlsx(ingresos_2017_2020,"ingresos_autonomo_hog_2017_2020.xlsx")

kbl(ingresos_2017_2020) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

 #### 2020

tabla_prom_reg <- data.frame()

for (i in reg_20) {
  casen_fh <- filter(casen2020, casen2020$region == i)
  
casen_fh <- casen_fh[,c("region","folio","ysubh")]
casen_fh$folio <- as.character(casen_fh$folio)
  
tabla <- table(casen_fh$folio)
tabla <- as.data.frame(tabla)
colnames(tabla) <- c("folio","freq_folio")
repetidos2 <- merge(x=casen_fh, y=tabla, by = "folio", all.x = T)
repetidos2 <- repetidos2[,c(2,1,4,3)]
rr <- repetidos2[!duplicated(repetidos2$folio ), ]

tabla_reg <- data.frame(
  region = i,
  promedio_2020 = mean(rr$ysubh)
)

tabla_prom_reg <- rbind(tabla_prom_reg, tabla_reg)

}


tabla_ing_aut <- read_xlsx("subsidios_monetarios_oficial_2020.xlsx")
names(tabla_ing_aut)[3] <- "region" 
names(tabla_ing_aut)[1] <- "region_c" 
 
tabla_2020 = merge(x = tabla_prom_reg, y = tabla_ing_aut, by = "region", all.x = T)
tabla_2020 <- tabla_2020[,-c(3)]
 
 #### 2017

tabla_prom_reg <- data.frame()

for (i in reg_17) {
  casen_fh <- filter(casen2017, casen2017$region == i)
  
casen_fh <- casen_fh[,c("region","folio","ysubh")]
casen_fh$folio <- as.character(casen_fh$folio)
  
tabla <- table(casen_fh$folio)
tabla <- as.data.frame(tabla)
colnames(tabla) <- c("folio","freq_folio")
repetidos2 <- merge(x=casen_fh, y=tabla, by = "folio", all.x = T)
repetidos2 <- repetidos2[,c(2,1,4,3)]
rr <- repetidos2[!duplicated(repetidos2$folio ), ]

tabla_reg <- data.frame(
  region = i,
  promedio_2017 = mean(rr$ysubh)
)

tabla_prom_reg <- rbind(tabla_prom_reg, tabla_reg)

}

tabla_ing_aut <- read_xlsx("subsidios_monetarios_oficial_2017.xlsx")
names(tabla_ing_aut)[3] <- "region" 
names(tabla_ing_aut)[1] <- "region_c" 
 
tabla_2017 = merge(x = tabla_prom_reg, y = tabla_ing_aut, by = "region", all.x = T)
tabla_2017 <- tabla_2017[,-c(3)]
tabla_2017$region[tabla_2017$region == "Región de Ayséndel Gral. Carlos Ibáñez del Campo"] <- "Región de Aysén del Gral. Carlos Ibáñez del Campo"
tabla_2017$region[tabla_2017$region == "Región del Libertador Gral. Bernardo O’Higgins"] <- "Región del Libertador Gral. Bernardo O'Higgins"

ingresos_2017_2020 <- cbind(tabla_2017,tabla_2020)
ingresos_2017_2020 <- ingresos_2017_2020[,-c(3,4,6)]
write_xlsx(ingresos_2017_2020,"ingresos_subsidios_monetarios_2017_2020.xlsx")

kbl(ingresos_2017_2020) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

 #### 2020

tabla_prom_reg <- data.frame()

for (i in reg_20) {
  casen_fh <- filter(casen2020, casen2020$region == i)
  
casen_fh <- casen_fh[,c("region","folio","ymonecorh")]
casen_fh$folio <- as.character(casen_fh$folio)
  
tabla <- table(casen_fh$folio)
tabla <- as.data.frame(tabla)
colnames(tabla) <- c("folio","freq_folio")
repetidos2 <- merge(x=casen_fh, y=tabla, by = "folio", all.x = T)
repetidos2 <- repetidos2[,c(2,1,4,3)]
rr <- repetidos2[!duplicated(repetidos2$folio ), ]

tabla_reg <- data.frame(
  region = i,
  promedio_2020 = mean(rr$ymonecorh)
)

tabla_prom_reg <- rbind(tabla_prom_reg, tabla_reg)

}

tabla_ing_aut <- read_xlsx("ingreso_monetario_oficial_2020.xlsx")
names(tabla_ing_aut)[3] <- "region" 
names(tabla_ing_aut)[1] <- "region_c" 
 
tabla_2020 = merge(x = tabla_prom_reg, y = tabla_ing_aut, by = "region", all.x = T)
tabla_2020 <- tabla_2020[,-c(3)]
 
 #### 2017

tabla_prom_reg <- data.frame()

for (i in reg_17) {
  casen_fh <- filter(casen2017, casen2017$region == i)
  
casen_fh <- casen_fh[,c("region","folio","ymonecorh")]
casen_fh$folio <- as.character(casen_fh$folio)
  
tabla <- table(casen_fh$folio)
tabla <- as.data.frame(tabla)
colnames(tabla) <- c("folio","freq_folio")
repetidos2 <- merge(x=casen_fh, y=tabla, by = "folio", all.x = T)
repetidos2 <- repetidos2[,c(2,1,4,3)]
rr <- repetidos2[!duplicated(repetidos2$folio ), ]

tabla_reg <- data.frame(
  region = i,
  promedio_2017 = mean(rr$ymonecorh)
)

tabla_prom_reg <- rbind(tabla_prom_reg, tabla_reg)

}

tabla_ing_aut <- read_xlsx("ingreso_monetario_oficial_2017.xlsx")
names(tabla_ing_aut)[3] <- "region" 
names(tabla_ing_aut)[1] <- "region_c" 
 
tabla_2017 = merge(x = tabla_prom_reg, y = tabla_ing_aut, by = "region", all.x = T)
tabla_2017 <- tabla_2017[,-c(3)]
tabla_2017$region[tabla_2017$region == "Región de Ayséndel Gral. Carlos Ibáñez del Campo"] <- "Región de Aysén del Gral. Carlos Ibáñez del Campo"
tabla_2017$region[tabla_2017$region == "Región del Libertador Gral. Bernardo O’Higgins"] <- "Región del Libertador Gral. Bernardo O'Higgins"

ingresos_2017_2020 <- cbind(tabla_2017,tabla_2020)
ingresos_2017_2020 <- ingresos_2017_2020[,-c(3,4,6)]
write_xlsx(ingresos_2017_2020,"ingresos_monetarios_hog_2017_2020.xlsx")

kbl(ingresos_2017_2020) %>%
  kable_styling(bootstrap_options = c("striped", "hover")) %>%
  kable_paper() %>%
  scroll_box(width = "100%", height = "300px")

casen2011_rm <- filter(casen2011, casen2011$region == "Metropolitana")

# comuna_ing <- unique(casen2011_rm$comuna)
tabla <- table(casen2011_rm$folio)
tabla <- as.data.frame(tabla)
colnames(tabla) <- c("folio","freq_folio")
head(tabla,10)

casen_fh <- casen2011_rm[,c("comuna","folio","yauthaj","expc_full","numper")]
casen_fh$folio <- as.character(casen_fh$folio)
repetidos2 <- merge(x=casen_fh, y=tabla, by = "folio", all.x = T)
#repetidos2 <- repetidos2[,c(2,1,6,4,5)]
rr <- repetidos2[!duplicated(repetidos2$folio), ]
head(rr,10)

rr$ing_per_h <- rr$yauthaj/rr$freq_folio

aaa <- aggregate(rr$ing_per_h, list(rr$comuna), FUN=mean)
names(aaa)[1]<-c("Comuna")
aaa$`mean.rr$ing_per_h` <- as.numeric(aaa$`mean.rr$ing_per_h`)
names(aaa)[2]<-c("calculo_propio")

write_xlsx(aaa, "aaaa.xlsx")

bbb <- read.csv2("ing_aut_percap_del_hogar_casen2011.csv")
bbb$`iaph` <- as.numeric(bbb$`iaph`)
names(bbb)[1] <- "Comuna"

ccc <- merge(x = aaa, y = bbb, by = "Comuna", all.x = TRUE)

p <- plot_ly(ccc, width = 1200, x = ~
Comuna, y = ~calculo_propio) %>%
  add_lines()
#p <- plot_ly(width = 800, height = 800)
p <- p %>% add_trace(y = ~iaph, name = 'iaph', mode = 'markers')
p